The paper by Cousijn et al. is an important addition to the expanding literature on the potential mechanism through which ZNF804A confers increased risk for psychosis. In their paper, Cousijn et al. reported that neither rs1344706 nor other SNPs typed in the ZNF804A gene affected any volumetric or voxel-based morphometry (VBM) parameter, suggesting that the association of ZNF804A with psychosis is unlikely mediated through an effect on brain structure. When the ZNF804A association with schizophrenia first appeared, we explored the modulation of brain structures with VBM by ZNF804A rs1344706 in a sample of healthy volunteers (N = 179). Our experience with other genes associated with risk for schizophrenia that also showed effects on brain volume measures in normal subjects (e.g., DISC1, COMT, BDNF, etc.) led us to expect similar results with ZNF804A. Instead, the results were decidedly negative (data not published), quite similar to those reported by Cousijn et al. We chose not to publish because no single sample can prove the negative. Given the much larger sample of the study by Cousijn et al. (892 healthy controls) and the two different analysis methods they implemented in exploring structural differences across genotype groups, the risk of a type II error seems insignificant, and the lack of association is likely a true negative result.

This negative result does not come as a surprise. If ZNF804A confers risk for schizophrenia through mechanisms detectable with actual neuroimaging techniques, it is predictable that it would do so through modulation of a neuroimaging intermediate phenotype, i.e., a neuroimaging trait abnormal in patients with schizophrenia but also in their healthy relatives, who share part of the genome with patients but not the confounding factors related to the state of the disorder.

The evidence that structural changes in patients with schizophrenia are trait measures related to genetic risk is not strong. The evidence that they are state factors related to the experience of illness is much more consistent. Despite robust brain morphological differences between patients and healthy volunteers, there is much less evidence for structural variation as intermediate phenotype, i.e., structural differences between siblings of patients with schizophrenia and healthy volunteers, especially when confounders are controlled. For instance, we explored samples of patients, siblings, and normal controls and did not detect differences in structural measurements between siblings and healthy volunteers, despite relevant sample sizes (N = 600) and different analytical methods (VBM, Honea et al., 2008; cortical thickness, Goldman et al., 2009; automated subcortical segmentation technique, Goldman et al., 2008).

We believe that restricting our investigation to identified intermediate phenotypes is a winning strategy when exploring modulatory effects of risk genes associated with schizophrenia. Indeed, because most genes are expressed in the brain, they likely will show effects on modulating brain function and structure, but this is not necessarily related to their biological mechanism of increasing the risk for the clinical illness. However, if the risk gene modulates an intermediate phenotype, it is likely that this effect is related to a biological mechanism that confers an increased risk for the disease, rather than a relatively unspecific effect of the gene on the brain.

Following this approach, in a recent study (Rasetti et al., 2011), we demonstrated that the functional coupling between PFC and hippocampus during a working memory task, previously shown to be modulated by ZNF804A rs1344706 (Esslinger et al., 2009), was a reliable neuroimaging intermediate phenotype. Without this new piece of information, it would have been difficult to disentangle a nonspecific brain effect of the gene from a specific biological mechanism of the gene likely involved in the pathophysiological process underlying the disorder.

Given intricate brain organization, nonspecific gene effects, and an increasing number of new risk genes, a focused, hypothesis-driven approach—such as risk gene modulation of intermediate phenotypes—seems to be a successful way to obtain meaningful information about disease mechanisms.

Although the recent study by Cousijn et al. provides a very large
sample size for an imaging-genetics study, their brain phenotypes based
on volumetric analyses, at least in cortical gray matter, are
confounded by the fact that cortical brain volume is a product of
cortical thickness and surface area. These dependent measures
(cortical thickness and surface area) are under different genetic (Panizzon et al., 2009), environmental (Raznahan et al., 2010), and cellular (Chenn and Walsh, 2002) control. Therefore, the use of volumetric phenotypes creates a challenge in determining whether a negative finding, even from a large study, is definitive in its assessment of the association of a genetic variant with brain structure.

This study by Cousijn et al. represents the largest structural MRI study of the effects of ZNF804A on brain volume to have been carried out to date. In a sample of 892 healthy young adults, voxel brain morphometry was carried out focusing on both whole brain, grey matter, white matter, and eight circumscribed brain structures: nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, hippocampus, putamen, and thalamus. Two hundred sixty-six common ZNF804A variants were considered, including the rs1344706 variant that remains the most strongly associated with schizophrenia. No significant—or even trend-level—associations were observed.

Since the original identification of ZNF804A almost four years ago, there has been a flurry of neurocognitive and neuroanatomical studies seeking to establish the neural mechanism(s) by which increased schizophrenia risk associated with this gene might be mediated at the level of either brain structure or function. The study by Cousijn et al. accords with a previously and similarly large neuropsychological study from our group Walters et al., 2010) to indicate that ZNF804A is not associated with deleterious consequences either for brain structure or for neuropsychological performance. By comparison, functional imaging studies from Esslinger and colleagues (Esslinger et al., 2009; Walter et al., 2011; Paulus et al., 2011; Rassetti et al., 2011) have indicated that ZNF804A is likely to be associated with disconnectivity, particularly between the right DLPFC and hippocampus. Similarly, there is evidence also that ZNF804A may influence processing of social stimuli such as is relevant to the theory of mind performance (Walter et al., 2011) or attributional style (Hargreaves et al., 2012).

Understanding what schizophrenia risk variants are not doing to brain structure/function is perhaps as relevant to understanding risk variants as understanding what they are doing, despite the obvious publication bias in this field. A number of possible explanations for this failure to identify ZNF804A’s effects on brain structure come to mind. The first—raised in the comment by Voineskos and colleagues—is that the measure of brain volume used (VBM) is insensitive to a genetic contribution to brain structural variation. We are not convinced by this argument in this case. While cortical thickness may offer some methodological advantages for studying cortical grey matter, ZNF804A’s influence on the range of subcortical structures investigated (described above) is less likely to have been missed by this measure.

An alternative possibility is that risk variants at ZNF804A simply don’t make a significant impact on brain volume in healthy participants. In our view, this would not be surprising for at least two reasons. First, an advantage of GWAS is its potential to implicate biological risk pathways that have not previously been hypothesized, and, hence, not previously explored. Unlike candidate genes targeted because of their previously known role in schizophrenia-relevant biological pathways (e.g., myelin integrity, NMDA receptor function, dopamine metabolism, etc.), variants such as ZNF804A may well contribute risk via novel mechanisms not associated with brain volume. Second, the risk allele identified for schizophrenia susceptibility has a very modest effect on illness risk (OR = 1.10). At least 10 common risk loci have now been implicated in schizophrenia susceptibility from the GWAS literature. A substantial proportion, perhaps more than a quarter, of the total variation in schizophrenia susceptibility may be explained by many more common risk variants of small effect (Lee et al., 2012). It is perhaps unsurprising that, individually, these risk alleles will have subtle rather than gross effects on brain structure and cognition. If an individual common variant was to exert a more deleterious effect, it is also uncertain why it would be under positive selection in the population.

In conclusion, this study is a welcome addition to the literature, given its sample size and clear conclusion. This approach allows the authors to helpfully avoid many of the caveats that plague the imaging genetics literature. At the same time, it challenges us to continue to develop cognitive and cortical approaches that allow us to characterize brain function and dysfunction, and capture where, how, and what in the brain is being deleteriously impacted by common GWAS identified risk variants in genes such ZNF804A.

Reply by authors to previous comments
First of all, we would like to thank the three research groups for their comments. As Corvin and Donohoe pointed out, it is important for the field to know what disease risk variants are not doing. Given the known structural changes in brain volume seen in schizophrenia, we decided to investigate the association of ZNF804A with brain volume. Our results show that the association between ZNF804A and schizophrenia is unlikely to be mediated by effects on brain volume variation. We were pleased with the comment of Rasetti and Weinberger, who confirmed our null finding in their sample. However, both studies do not exclude the possibility that differences might be found in patients. Voineskos and Kennedy are right in pointing out that our study does not provide evidence that there is no effect of variation in ZNF804A on cortical thickness; it would be of interest to study this in a large sample. Also, looking at white matter differences as measured by DTI in a sufficiently large sample might provide us with more insight into the mechanisms of ZNF804A. Although, as Corvin and Donohoe suggested, the effects of the risk allele might be too subtle to detect with structural brain imaging measures, and might therefore have to be studied at the molecular level.